Overview

Dataset statistics

Number of variables32
Number of observations3068040
Missing cells69864605
Missing cells (%)71.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory749.0 MiB
Average record size in memory256.0 B

Variable types

Text1
Numeric25
Unsupported1
Categorical3
Boolean2

Alerts

step_mod has constant value ""Constant
delinq_disaster has constant value ""Constant
year_month is highly overall correlated with loan_age and 5 other fieldsHigh correlation
curr_upb is highly overall correlated with interest_bearing_upbHigh correlation
loan_age is highly overall correlated with year_month and 4 other fieldsHigh correlation
month_remaining_LM is highly overall correlated with year_month and 5 other fieldsHigh correlation
defect_settlement is highly overall correlated with year_month and 6 other fieldsHigh correlation
zero_bal_code is highly overall correlated with mod_flagHigh correlation
zero_bal_date is highly overall correlated with year_month and 5 other fieldsHigh correlation
curr_int is highly overall correlated with mod_cost and 1 other fieldsHigh correlation
ddlpi is highly overall correlated with year_month and 5 other fieldsHigh correlation
mi_rec is highly overall correlated with mod_flag and 1 other fieldsHigh correlation
net_sales is highly overall correlated with zero_bal_removal_upb and 3 other fieldsHigh correlation
non_mi_rec is highly overall correlated with mod_flag and 1 other fieldsHigh correlation
expenses is highly overall correlated with legal_costs and 7 other fieldsHigh correlation
legal_costs is highly overall correlated with expenses and 5 other fieldsHigh correlation
maint_costs is highly overall correlated with expenses and 3 other fieldsHigh correlation
tax_ins is highly overall correlated with expenses and 5 other fieldsHigh correlation
misc_expenses is highly overall correlated with expenses and 2 other fieldsHigh correlation
actual_loss is highly overall correlated with delinq_int and 2 other fieldsHigh correlation
mod_cost is highly overall correlated with defect_settlement and 6 other fieldsHigh correlation
eltv is highly overall correlated with month_remaining_LMHigh correlation
zero_bal_removal_upb is highly overall correlated with net_sales and 1 other fieldsHigh correlation
delinq_int is highly overall correlated with net_sales and 6 other fieldsHigh correlation
curr_month_mod_cost is highly overall correlated with curr_int and 1 other fieldsHigh correlation
interest_bearing_upb is highly overall correlated with curr_upbHigh correlation
mod_flag is highly overall correlated with defect_settlement and 15 other fieldsHigh correlation
deferred_payment_plan is highly overall correlated with mi_rec and 9 other fieldsHigh correlation
borrower_assistance is highly overall correlated with year_month and 1 other fieldsHigh correlation
mod_flag is highly imbalanced (85.2%)Imbalance
deferred_payment_plan is highly imbalanced (71.7%)Imbalance
defect_settlement has 3067897 (> 99.9%) missing valuesMissing
mod_flag has 3045608 (99.3%) missing valuesMissing
zero_bal_code has 3021495 (98.5%) missing valuesMissing
zero_bal_date has 3021495 (98.5%) missing valuesMissing
ddlpi has 3062559 (99.8%) missing valuesMissing
mi_rec has 3067497 (> 99.9%) missing valuesMissing
net_sales has 3067495 (> 99.9%) missing valuesMissing
non_mi_rec has 3067497 (> 99.9%) missing valuesMissing
expenses has 3067497 (> 99.9%) missing valuesMissing
legal_costs has 3067497 (> 99.9%) missing valuesMissing
maint_costs has 3067497 (> 99.9%) missing valuesMissing
tax_ins has 3067497 (> 99.9%) missing valuesMissing
misc_expenses has 3067497 (> 99.9%) missing valuesMissing
actual_loss has 3067495 (> 99.9%) missing valuesMissing
mod_cost has 3067580 (> 99.9%) missing valuesMissing
step_mod has 3067566 (> 99.9%) missing valuesMissing
deferred_payment_plan has 3063801 (99.9%) missing valuesMissing
eltv has 2581932 (84.2%) missing valuesMissing
zero_bal_removal_upb has 3021495 (98.5%) missing valuesMissing
delinq_int has 3067371 (> 99.9%) missing valuesMissing
delinq_disaster has 3064125 (99.9%) missing valuesMissing
borrower_assistance has 3062304 (99.8%) missing valuesMissing
curr_month_mod_cost has 3041908 (99.1%) missing valuesMissing
curr_deferred_upb is highly skewed (γ1 = 51.20668043)Skewed
curr_loan_delinq is an unsupported type, check if it needs cleaning or further analysisUnsupported
curr_upb has 46545 (1.5%) zerosZeros
loan_age has 45159 (1.5%) zerosZeros
curr_deferred_upb has 3061632 (99.8%) zerosZeros
interest_bearing_upb has 46545 (1.5%) zerosZeros

Reproduction

Analysis started2023-11-13 19:40:14.052823
Analysis finished2023-11-13 19:46:33.208475
Duration6 minutes and 19.16 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Distinct49998
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:33.535254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters36816480
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowF09Q10000013
2nd rowF09Q10000013
3rd rowF09Q10000013
4th rowF09Q10000013
5th rowF09Q10000013
ValueCountFrequency (%)
f09q10193280 170
 
< 0.1%
f09q10029420 170
 
< 0.1%
f09q10125466 170
 
< 0.1%
f09q10139325 170
 
< 0.1%
f09q10036449 170
 
< 0.1%
f09q10023250 170
 
< 0.1%
f09q10043714 170
 
< 0.1%
f09q10111127 170
 
< 0.1%
f09q10111248 170
 
< 0.1%
f09q10060099 170
 
< 0.1%
Other values (49988) 3066340
99.9%
2023-11-13T11:46:34.121496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8260960
22.4%
9 4574870
12.4%
F 3068040
 
8.3%
Q 3068040
 
8.3%
2 2925726
 
7.9%
3 2921976
 
7.9%
1 2839724
 
7.7%
4 2748838
 
7.5%
5 1718646
 
4.7%
6 1653645
 
4.5%
Other values (2) 3036015
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30680400
83.3%
Uppercase Letter 6136080
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8260960
26.9%
9 4574870
14.9%
2 2925726
 
9.5%
3 2921976
 
9.5%
1 2839724
 
9.3%
4 2748838
 
9.0%
5 1718646
 
5.6%
6 1653645
 
5.4%
7 1526780
 
5.0%
8 1509235
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
F 3068040
50.0%
Q 3068040
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30680400
83.3%
Latin 6136080
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8260960
26.9%
9 4574870
14.9%
2 2925726
 
9.5%
3 2921976
 
9.5%
1 2839724
 
9.3%
4 2748838
 
9.0%
5 1718646
 
5.6%
6 1653645
 
5.4%
7 1526780
 
5.0%
8 1509235
 
4.9%
Latin
ValueCountFrequency (%)
F 3068040
50.0%
Q 3068040
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36816480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8260960
22.4%
9 4574870
12.4%
F 3068040
 
8.3%
Q 3068040
 
8.3%
2 2925726
 
7.9%
3 2921976
 
7.9%
1 2839724
 
7.7%
4 2748838
 
7.5%
5 1718646
 
4.7%
6 1653645
 
4.5%
Other values (2) 3036015
 
8.2%

year_month
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201307.85
Minimum200902
Maximum202303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:34.362687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200902
5-th percentile200910
Q1201011
median201206
Q3201506
95-th percentile202006
Maximum202303
Range1401
Interquartile range (IQR)495

Descriptive statistics

Standard deviation335.32616
Coefficient of variation (CV)0.0016657381
Kurtosis0.061064107
Mean201307.85
Median Absolute Deviation (MAD)200
Skewness0.95596924
Sum6.1762054 × 1011
Variance112443.63
MonotonicityNot monotonic
2023-11-13T11:46:34.647171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201003 48826
 
1.6%
201002 48781
 
1.6%
201004 48681
 
1.6%
201005 48524
 
1.6%
201001 48466
 
1.6%
201006 48370
 
1.6%
201007 48084
 
1.6%
201008 47672
 
1.6%
201009 46960
 
1.5%
201010 46082
 
1.5%
Other values (160) 2587594
84.3%
ValueCountFrequency (%)
200902 2897
 
0.1%
200903 7369
 
0.2%
200904 12037
 
0.4%
200905 16059
 
0.5%
200906 20189
0.7%
200907 24032
0.8%
200908 29415
1.0%
200909 33278
1.1%
200910 36670
1.2%
200911 40466
1.3%
ValueCountFrequency (%)
202303 3495
0.1%
202302 3509
0.1%
202301 3548
0.1%
202212 3586
0.1%
202211 3626
0.1%
202210 3668
0.1%
202209 3725
0.1%
202208 3779
0.1%
202207 3824
0.1%
202206 3869
0.1%

curr_upb
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2239966
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169449.64
Minimum0
Maximum790000
Zeros46545
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:35.122928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36146.987
Q190944.61
median146153.61
Q3227857.28
95-th percentile383884.79
Maximum790000
Range790000
Interquartile range (IQR)136912.67

Descriptive statistics

Standard deviation107107.1
Coefficient of variation (CV)0.63208807
Kurtosis1.3500548
Mean169449.64
Median Absolute Deviation (MAD)64001.13
Skewness1.0485595
Sum5.1987827 × 1011
Variance1.147193 × 1010
MonotonicityNot monotonic
2023-11-13T11:46:35.498219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46545
 
1.5%
415000 3735
 
0.1%
416000 3719
 
0.1%
414000 3354
 
0.1%
100000 2694
 
0.1%
199000 2360
 
0.1%
99000 2072
 
0.1%
149000 2015
 
0.1%
417000 1993
 
0.1%
200000 1802
 
0.1%
Other values (2239956) 2997751
97.7%
ValueCountFrequency (%)
0 46545
1.5%
0.01 1
 
< 0.1%
0.14 1
 
< 0.1%
1 1
 
< 0.1%
3.14 1
 
< 0.1%
3.58 1
 
< 0.1%
5.18 1
 
< 0.1%
10.34 1
 
< 0.1%
11.99 1
 
< 0.1%
16.8 1
 
< 0.1%
ValueCountFrequency (%)
790000 1
 
< 0.1%
789000 1
 
< 0.1%
788000 2
< 0.1%
787000 2
< 0.1%
786000 3
< 0.1%
785000 1
 
< 0.1%
784000 2
< 0.1%
783000 1
 
< 0.1%
782959.35 1
 
< 0.1%
782000 1
 
< 0.1%

curr_loan_delinq
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size23.4 MiB

loan_age
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct170
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.38163
Minimum0
Maximum169
Zeros45159
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:35.786856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median34
Q369
95-th percentile129
Maximum169
Range169
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.414968
Coefficient of variation (CV)0.84979696
Kurtosis0.1575117
Mean46.38163
Median Absolute Deviation (MAD)23
Skewness1.0035193
Sum1.423007 × 108
Variance1553.5397
MonotonicityNot monotonic
2023-11-13T11:46:36.056648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 50224
 
1.6%
2 50156
 
1.6%
4 50134
 
1.6%
5 49993
 
1.6%
6 49846
 
1.6%
7 49680
 
1.6%
1 49491
 
1.6%
8 49441
 
1.6%
9 49149
 
1.6%
10 48773
 
1.6%
Other values (160) 2571153
83.8%
ValueCountFrequency (%)
0 45159
1.5%
1 49491
1.6%
2 50156
1.6%
3 50224
1.6%
4 50134
1.6%
5 49993
1.6%
6 49846
1.6%
7 49680
1.6%
8 49441
1.6%
9 49149
1.6%
ValueCountFrequency (%)
169 130
 
< 0.1%
168 326
 
< 0.1%
167 573
 
< 0.1%
166 789
 
< 0.1%
165 1076
 
< 0.1%
164 1411
< 0.1%
163 1790
0.1%
162 2071
0.1%
161 2358
0.1%
160 2724
0.1%

month_remaining_LM
Real number (ℝ)

HIGH CORRELATION 

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.45411
Minimum0
Maximum480
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:36.390699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1230
median309
Q3340
95-th percentile357
Maximum480
Range480
Interquartile range (IQR)110

Descriptive statistics

Standard deviation83.399474
Coefficient of variation (CV)0.30167565
Kurtosis0.14817279
Mean276.45411
Median Absolute Deviation (MAD)39
Skewness-1.0209931
Sum8.4817228 × 108
Variance6955.4723
MonotonicityNot monotonic
2023-11-13T11:46:36.660268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
357 37986
 
1.2%
358 37976
 
1.2%
356 37910
 
1.2%
355 37799
 
1.2%
354 37690
 
1.2%
359 37598
 
1.2%
353 37583
 
1.2%
352 37409
 
1.2%
351 37211
 
1.2%
350 36932
 
1.2%
Other values (471) 2691946
87.7%
ValueCountFrequency (%)
0 18
 
< 0.1%
1 36
< 0.1%
2 42
< 0.1%
3 47
< 0.1%
4 50
< 0.1%
5 53
< 0.1%
6 56
< 0.1%
7 57
< 0.1%
8 61
< 0.1%
9 65
< 0.1%
ValueCountFrequency (%)
480 32
 
< 0.1%
479 323
< 0.1%
478 396
< 0.1%
477 424
< 0.1%
476 430
< 0.1%
475 435
< 0.1%
474 436
< 0.1%
473 431
< 0.1%
472 430
< 0.1%
471 429
< 0.1%

defect_settlement
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)32.2%
Missing3067897
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean201173.33
Minimum200911
Maximum202005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:36.901512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200911
5-th percentile201006.1
Q1201103
median201106
Q3201205
95-th percentile201506.8
Maximum202005
Range1094
Interquartile range (IQR)102

Descriptive statistics

Standard deviation174.23748
Coefficient of variation (CV)0.00086610629
Kurtosis4.7658952
Mean201173.33
Median Absolute Deviation (MAD)97
Skewness1.8910355
Sum28767786
Variance30358.701
MonotonicityNot monotonic
2023-11-13T11:46:37.083965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
201106 31
 
< 0.1%
201205 18
 
< 0.1%
201104 9
 
< 0.1%
201009 6
 
< 0.1%
201011 5
 
< 0.1%
201012 5
 
< 0.1%
201101 4
 
< 0.1%
201102 4
 
< 0.1%
201204 3
 
< 0.1%
201109 3
 
< 0.1%
Other values (36) 55
 
< 0.1%
(Missing) 3067897
> 99.9%
ValueCountFrequency (%)
200911 3
< 0.1%
201002 2
 
< 0.1%
201004 1
 
< 0.1%
201005 1
 
< 0.1%
201006 1
 
< 0.1%
201007 2
 
< 0.1%
201008 1
 
< 0.1%
201009 6
< 0.1%
201010 1
 
< 0.1%
201011 5
< 0.1%
ValueCountFrequency (%)
202005 1
< 0.1%
201801 1
< 0.1%
201705 2
< 0.1%
201604 1
< 0.1%
201603 1
< 0.1%
201510 1
< 0.1%
201507 1
< 0.1%
201505 2
< 0.1%
201501 1
< 0.1%
201409 2
< 0.1%

mod_flag
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3045608
Missing (%)99.3%
Memory size23.4 MiB
P
21958 
Y
 
474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22432
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowP
3rd rowP
4th rowP
5th rowP

Common Values

ValueCountFrequency (%)
P 21958
 
0.7%
Y 474
 
< 0.1%
(Missing) 3045608
99.3%

Length

2023-11-13T11:46:37.253018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T11:46:37.403082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
p 21958
97.9%
y 474
 
2.1%

Most occurring characters

ValueCountFrequency (%)
P 21958
97.9%
Y 474
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22432
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 21958
97.9%
Y 474
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 22432
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 21958
97.9%
Y 474
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 21958
97.9%
Y 474
 
2.1%

zero_bal_code
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing3021495
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean1.3692126
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:37.524868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum96
Range95
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1025621
Coefficient of variation (CV)3.7266397
Kurtosis325.17081
Mean1.3692126
Median Absolute Deviation (MAD)0
Skewness17.779564
Sum63730
Variance26.03614
MonotonicityNot monotonic
2023-11-13T11:46:37.653548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 45746
 
1.5%
9 256
 
< 0.1%
3 134
 
< 0.1%
96 129
 
< 0.1%
16 124
 
< 0.1%
2 110
 
< 0.1%
15 46
 
< 0.1%
(Missing) 3021495
98.5%
ValueCountFrequency (%)
1 45746
1.5%
2 110
 
< 0.1%
3 134
 
< 0.1%
9 256
 
< 0.1%
15 46
 
< 0.1%
16 124
 
< 0.1%
96 129
 
< 0.1%
ValueCountFrequency (%)
96 129
 
< 0.1%
16 124
 
< 0.1%
15 46
 
< 0.1%
9 256
 
< 0.1%
3 134
 
< 0.1%
2 110
 
< 0.1%
1 45746
1.5%

zero_bal_date
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct169
Distinct (%)0.4%
Missing3021495
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean201354.42
Minimum200903
Maximum202303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:37.826110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200903
5-th percentile201008
Q1201111
median201212
Q3201507
95-th percentile202011
Maximum202303
Range1400
Interquartile range (IQR)396

Descriptive statistics

Standard deviation312.77207
Coefficient of variation (CV)0.001553341
Kurtosis0.33374613
Mean201354.42
Median Absolute Deviation (MAD)111
Skewness1.0588122
Sum9.3720416 × 109
Variance97826.369
MonotonicityNot monotonic
2023-11-13T11:46:38.012453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201011 1072
 
< 0.1%
201210 1021
 
< 0.1%
201110 1014
 
< 0.1%
201111 1010
 
< 0.1%
201203 972
 
< 0.1%
201208 968
 
< 0.1%
201010 944
 
< 0.1%
201202 926
 
< 0.1%
201112 919
 
< 0.1%
201209 913
 
< 0.1%
Other values (159) 36786
 
1.2%
(Missing) 3021495
98.5%
ValueCountFrequency (%)
200903 4
 
< 0.1%
200904 17
 
< 0.1%
200905 37
 
< 0.1%
200906 48
 
< 0.1%
200907 42
 
< 0.1%
200908 43
 
< 0.1%
200909 48
 
< 0.1%
200910 100
< 0.1%
200911 150
< 0.1%
200912 181
< 0.1%
ValueCountFrequency (%)
202303 42
< 0.1%
202302 14
 
< 0.1%
202301 39
< 0.1%
202212 38
< 0.1%
202211 40
< 0.1%
202210 42
< 0.1%
202209 57
< 0.1%
202208 54
< 0.1%
202207 45
< 0.1%
202206 45
< 0.1%

curr_int
Real number (ℝ)

HIGH CORRELATION 

Distinct266
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.968149
Minimum2
Maximum7.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:38.224877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.375
Q14.75
median4.875
Q35.25
95-th percentile5.625
Maximum7.875
Range5.875
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.40295387
Coefficient of variation (CV)0.081107444
Kurtosis1.2530247
Mean4.968149
Median Absolute Deviation (MAD)0.25
Skewness0.65682423
Sum15242480
Variance0.16237182
MonotonicityNot monotonic
2023-11-13T11:46:38.466135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.875 573990
18.7%
4.75 472422
15.4%
5 295921
9.6%
5.25 270076
8.8%
5.375 213829
 
7.0%
5.125 206153
 
6.7%
4.5 192918
 
6.3%
4.625 181404
 
5.9%
5.5 142788
 
4.7%
4.375 126423
 
4.1%
Other values (256) 392116
12.8%
ValueCountFrequency (%)
2 38
 
< 0.1%
2.875 115
 
< 0.1%
3 60
 
< 0.1%
3.125 393
 
< 0.1%
3.25 71
 
< 0.1%
3.5 170
 
< 0.1%
3.625 164
 
< 0.1%
3.75 687
 
< 0.1%
3.875 779
 
< 0.1%
4 5790
0.2%
ValueCountFrequency (%)
7.875 11
 
< 0.1%
7.5 39
 
< 0.1%
7.375 107
 
< 0.1%
7.25 186
 
< 0.1%
7.125 254
 
< 0.1%
7 125
 
< 0.1%
6.875 767
 
< 0.1%
6.75 1165
 
< 0.1%
6.625 2883
0.1%
6.5 5559
0.2%

curr_deferred_upb
Real number (ℝ)

SKEWED  ZEROS 

Distinct256
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.700474
Minimum0
Maximum146747.84
Zeros3061632
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:38.786187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum146747.84
Range146747.84
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1384.1635
Coefficient of variation (CV)34.008535
Kurtosis3371.345
Mean40.700474
Median Absolute Deviation (MAD)0
Skewness51.20668
Sum1.2487068 × 108
Variance1915908.6
MonotonicityNot monotonic
2023-11-13T11:46:39.037245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3061632
99.8%
1402.22 118
 
< 0.1%
13670.22 115
 
< 0.1%
43076.8 110
 
< 0.1%
49272.47 92
 
< 0.1%
31314.81 88
 
< 0.1%
8731.79 86
 
< 0.1%
65484.29 83
 
< 0.1%
15487.65 81
 
< 0.1%
73056.13 78
 
< 0.1%
Other values (246) 5557
 
0.2%
ValueCountFrequency (%)
0 3061632
99.8%
381.03 32
 
< 0.1%
381.14 33
 
< 0.1%
508.74 15
 
< 0.1%
671.51 2
 
< 0.1%
758.62 51
 
< 0.1%
807.98 5
 
< 0.1%
1009.72 22
 
< 0.1%
1135.24 7
 
< 0.1%
1143.43 2
 
< 0.1%
ValueCountFrequency (%)
146747.84 41
< 0.1%
90012.55 47
< 0.1%
88591.48 51
< 0.1%
75052.94 47
< 0.1%
74881.79 1
 
< 0.1%
74706.29 41
< 0.1%
73056.13 78
< 0.1%
68615.11 34
< 0.1%
67895.37 18
 
< 0.1%
65545.51 44
< 0.1%

ddlpi
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct170
Distinct (%)3.1%
Missing3062559
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean201921.19
Minimum200201
Maximum204001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:39.308286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200201
5-th percentile201109
Q1201908
median202008
Q3202106
95-th percentile202208
Maximum204001
Range3800
Interquartile range (IQR)198

Descriptive statistics

Standard deviation317.26894
Coefficient of variation (CV)0.0015712513
Kurtosis2.2587441
Mean201921.19
Median Absolute Deviation (MAD)99
Skewness-1.6421073
Sum1.10673 × 109
Variance100659.58
MonotonicityNot monotonic
2023-11-13T11:46:39.582925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202010 169
 
< 0.1%
202009 154
 
< 0.1%
202008 154
 
< 0.1%
202007 151
 
< 0.1%
202005 148
 
< 0.1%
202104 145
 
< 0.1%
202012 140
 
< 0.1%
202103 137
 
< 0.1%
202004 136
 
< 0.1%
202006 130
 
< 0.1%
Other values (160) 4017
 
0.1%
(Missing) 3062559
99.8%
ValueCountFrequency (%)
200201 1
 
< 0.1%
200905 2
 
< 0.1%
200908 1
 
< 0.1%
200909 3
 
< 0.1%
200910 4
 
< 0.1%
200911 4
 
< 0.1%
200912 5
< 0.1%
201001 8
< 0.1%
201002 11
< 0.1%
201003 8
< 0.1%
ValueCountFrequency (%)
204001 1
 
< 0.1%
202410 3
 
< 0.1%
202409 1
 
< 0.1%
202309 1
 
< 0.1%
202305 1
 
< 0.1%
202304 2
 
< 0.1%
202303 32
< 0.1%
202302 20
< 0.1%
202301 26
< 0.1%
202212 36
< 0.1%

mi_rec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)9.9%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4773.8838
Minimum0
Maximum124594.73
Zeros490
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:39.873520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile43423.463
Maximum124594.73
Range124594.73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16244.315
Coefficient of variation (CV)3.4027462
Kurtosis14.940713
Mean4773.8838
Median Absolute Deviation (MAD)0
Skewness3.7790762
Sum2592218.9
Variance2.6387777 × 108
MonotonicityNot monotonic
2023-11-13T11:46:41.250036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 490
 
< 0.1%
34886.58 1
 
< 0.1%
7656.48 1
 
< 0.1%
62002.15 1
 
< 0.1%
22085.83 1
 
< 0.1%
11497.93 1
 
< 0.1%
54866.64 1
 
< 0.1%
19980.84 1
 
< 0.1%
51957.76 1
 
< 0.1%
59512.82 1
 
< 0.1%
Other values (44) 44
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
0 490
< 0.1%
7656.48 1
 
< 0.1%
11497.93 1
 
< 0.1%
16923.07 1
 
< 0.1%
19980.84 1
 
< 0.1%
21152.26 1
 
< 0.1%
22085.83 1
 
< 0.1%
26441.24 1
 
< 0.1%
26972.29 1
 
< 0.1%
27146.41 1
 
< 0.1%
ValueCountFrequency (%)
124594.73 1
< 0.1%
92612.42 1
< 0.1%
87672.45 1
< 0.1%
86316.55 1
< 0.1%
82380.99 1
< 0.1%
78947.86 1
< 0.1%
76640.65 1
< 0.1%
73901.86 1
< 0.1%
70864.64 1
< 0.1%
67875.19 1
< 0.1%

net_sales
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct529
Distinct (%)97.1%
Missing3067495
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean138018.72
Minimum0
Maximum663087.46
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:41.425073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16140.672
Q169084.73
median124358.03
Q3197069.65
95-th percentile306585.37
Maximum663087.46
Range663087.46
Interquartile range (IQR)127984.92

Descriptive statistics

Standard deviation91931.557
Coefficient of variation (CV)0.66608034
Kurtosis2.3003498
Mean138018.72
Median Absolute Deviation (MAD)63372.03
Skewness1.0463539
Sum75220203
Variance8.4514111 × 109
MonotonicityNot monotonic
2023-11-13T11:46:41.596800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
< 0.1%
50000 2
 
< 0.1%
49000 2
 
< 0.1%
112382.72 1
 
< 0.1%
88708.45 1
 
< 0.1%
160748.08 1
 
< 0.1%
190374.54 1
 
< 0.1%
28260.79 1
 
< 0.1%
280644.52 1
 
< 0.1%
78770 1
 
< 0.1%
Other values (519) 519
 
< 0.1%
(Missing) 3067495
> 99.9%
ValueCountFrequency (%)
0 15
< 0.1%
876 1
 
< 0.1%
1597.91 1
 
< 0.1%
2835.52 1
 
< 0.1%
6489.18 1
 
< 0.1%
7100 1
 
< 0.1%
8139.55 1
 
< 0.1%
10670.99 1
 
< 0.1%
10994.87 1
 
< 0.1%
11250 1
 
< 0.1%
ValueCountFrequency (%)
663087.46 1
< 0.1%
581000 1
< 0.1%
462062.7 1
< 0.1%
406654.5 1
< 0.1%
396127.33 1
< 0.1%
359546.52 1
< 0.1%
352104.2 1
< 0.1%
350413.81 1
< 0.1%
350189.9 1
< 0.1%
348847.91 1
< 0.1%

non_mi_rec
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct419
Distinct (%)77.2%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2333.905
Minimum0
Maximum121398.74
Zeros123
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:41.774575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.405
median993.39
Q32257.305
95-th percentile6557.014
Maximum121398.74
Range121398.74
Interquartile range (IQR)2213.9

Descriptive statistics

Standard deviation8081.0571
Coefficient of variation (CV)3.4624619
Kurtosis142.23645
Mean2333.905
Median Absolute Deviation (MAD)993.39
Skewness11.254543
Sum1267310.4
Variance65303484
MonotonicityNot monotonic
2023-11-13T11:46:41.989555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
< 0.1%
1 2
 
< 0.1%
0.02 2
 
< 0.1%
5911.79 1
 
< 0.1%
625.28 1
 
< 0.1%
8254.43 1
 
< 0.1%
645.54 1
 
< 0.1%
4756.72 1
 
< 0.1%
1825.84 1
 
< 0.1%
188.15 1
 
< 0.1%
Other values (409) 409
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
0 123
< 0.1%
0.01 1
 
< 0.1%
0.02 2
 
< 0.1%
0.11 1
 
< 0.1%
0.23 1
 
< 0.1%
1 2
 
< 0.1%
2.63 1
 
< 0.1%
10 1
 
< 0.1%
11.85 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
121398.74 1
< 0.1%
93328.82 1
< 0.1%
90290.03 1
< 0.1%
40190.39 1
< 0.1%
26126.28 1
< 0.1%
22043.9 1
< 0.1%
16703.66 1
< 0.1%
13979.09 1
< 0.1%
13896.23 1
< 0.1%
13040.55 1
< 0.1%

expenses
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct511
Distinct (%)94.1%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-18020.092
Minimum-185779.28
Maximum0
Zeros17
Zeros (%)< 0.1%
Negative526
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:42.261319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-185779.28
5-th percentile-49140.94
Q1-24939.975
median-12802.72
Q3-5404.67
95-th percentile-145
Maximum0
Range185779.28
Interquartile range (IQR)19535.305

Descriptive statistics

Standard deviation18924.657
Coefficient of variation (CV)-1.0501976
Kurtosis16.596275
Mean-18020.092
Median Absolute Deviation (MAD)8465.82
Skewness-2.9852566
Sum-9784910
Variance3.5814265 × 108
MonotonicityNot monotonic
2023-11-13T11:46:42.501707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
< 0.1%
-145 10
 
< 0.1%
-110 4
 
< 0.1%
-290 3
 
< 0.1%
-200 3
 
< 0.1%
-8480.18 1
 
< 0.1%
-1728.53 1
 
< 0.1%
-8404.59 1
 
< 0.1%
-15307.64 1
 
< 0.1%
-392.5 1
 
< 0.1%
Other values (501) 501
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
-185779.28 1
< 0.1%
-147344.86 1
< 0.1%
-100575.77 1
< 0.1%
-97324.12 1
< 0.1%
-88731.08 1
< 0.1%
-84652.98 1
< 0.1%
-81783.07 1
< 0.1%
-80113.68 1
< 0.1%
-78652.13 1
< 0.1%
-74240.26 1
< 0.1%
ValueCountFrequency (%)
0 17
< 0.1%
-25 1
 
< 0.1%
-85 1
 
< 0.1%
-110 4
 
< 0.1%
-145 10
< 0.1%
-149.01 1
 
< 0.1%
-170 1
 
< 0.1%
-171 1
 
< 0.1%
-200 3
 
< 0.1%
-230 1
 
< 0.1%

legal_costs
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct460
Distinct (%)84.7%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-3445.1848
Minimum-48144.66
Maximum0
Zeros79
Zeros (%)< 0.1%
Negative464
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:42.770790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-48144.66
5-th percentile-8348.03
Q1-4481.37
median-3018.22
Q3-1293.855
95-th percentile0
Maximum0
Range48144.66
Interquartile range (IQR)3187.515

Descriptive statistics

Standard deviation3499.6451
Coefficient of variation (CV)-1.0158077
Kurtosis50.407235
Mean-3445.1848
Median Absolute Deviation (MAD)1589.16
Skewness-4.7720353
Sum-1870735.3
Variance12247516
MonotonicityNot monotonic
2023-11-13T11:46:43.013488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
< 0.1%
-1 2
 
< 0.1%
-2032.73 2
 
< 0.1%
-590 2
 
< 0.1%
-75 2
 
< 0.1%
-282.5 2
 
< 0.1%
-799 1
 
< 0.1%
-3770.42 1
 
< 0.1%
-1435 1
 
< 0.1%
-8174.27 1
 
< 0.1%
Other values (450) 450
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
-48144.66 1
< 0.1%
-18548.03 1
< 0.1%
-18546.83 1
< 0.1%
-18026.49 1
< 0.1%
-17388 1
< 0.1%
-14945 1
< 0.1%
-13644.3 1
< 0.1%
-13478.62 1
< 0.1%
-13462.1 1
< 0.1%
-12762.5 1
< 0.1%
ValueCountFrequency (%)
0 79
< 0.1%
-1 2
 
< 0.1%
-15 1
 
< 0.1%
-25 1
 
< 0.1%
-30 1
 
< 0.1%
-68 1
 
< 0.1%
-75 2
 
< 0.1%
-85 1
 
< 0.1%
-87 1
 
< 0.1%
-185 1
 
< 0.1%

maint_costs
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct406
Distinct (%)74.8%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-5999.283
Minimum-62820.81
Maximum0
Zeros71
Zeros (%)< 0.1%
Negative472
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:43.291337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-62820.81
5-th percentile-24325.146
Q1-8635.935
median-1424.25
Q3-90
95-th percentile0
Maximum0
Range62820.81
Interquartile range (IQR)8545.935

Descriptive statistics

Standard deviation9184.6078
Coefficient of variation (CV)-1.5309509
Kurtosis6.7835948
Mean-5999.283
Median Absolute Deviation (MAD)1424.25
Skewness-2.2992225
Sum-3257610.7
Variance84357020
MonotonicityNot monotonic
2023-11-13T11:46:43.535334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
< 0.1%
-80 8
 
< 0.1%
-30 6
 
< 0.1%
-135 6
 
< 0.1%
-75 5
 
< 0.1%
-90 5
 
< 0.1%
-60 5
 
< 0.1%
-105 5
 
< 0.1%
-45 4
 
< 0.1%
-165 4
 
< 0.1%
Other values (396) 424
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
-62820.81 1
< 0.1%
-59058.09 1
< 0.1%
-48217.73 1
< 0.1%
-42949.9 1
< 0.1%
-41948.43 1
< 0.1%
-39775.79 1
< 0.1%
-39588.02 1
< 0.1%
-35688.81 1
< 0.1%
-35058.1 1
< 0.1%
-34283.24 1
< 0.1%
ValueCountFrequency (%)
0 71
< 0.1%
-5 1
 
< 0.1%
-10 3
 
< 0.1%
-13 1
 
< 0.1%
-13.5 2
 
< 0.1%
-15 1
 
< 0.1%
-16 2
 
< 0.1%
-20 2
 
< 0.1%
-21.9 1
 
< 0.1%
-26 1
 
< 0.1%

tax_ins
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct466
Distinct (%)85.8%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-7508.541
Minimum-168026.28
Maximum5260.59
Zeros78
Zeros (%)< 0.1%
Negative456
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:43.761101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-168026.28
5-th percentile-23937.585
Q1-9241.195
median-4236.02
Q3-1417.04
95-th percentile0
Maximum5260.59
Range173286.87
Interquartile range (IQR)7824.155

Descriptive statistics

Standard deviation12169.4
Coefficient of variation (CV)-1.620741
Kurtosis61.362874
Mean-7508.541
Median Absolute Deviation (MAD)3563.16
Skewness-6.0378937
Sum-4077137.8
Variance1.480943 × 108
MonotonicityNot monotonic
2023-11-13T11:46:43.963173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
< 0.1%
-75328.58 1
 
< 0.1%
-3793.09 1
 
< 0.1%
-1575.07 1
 
< 0.1%
-17324.05 1
 
< 0.1%
-6319.02 1
 
< 0.1%
-4927.1 1
 
< 0.1%
-114.03 1
 
< 0.1%
-675.43 1
 
< 0.1%
-13581.51 1
 
< 0.1%
Other values (456) 456
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
-168026.28 1
< 0.1%
-76523.85 1
< 0.1%
-75328.58 1
< 0.1%
-61537.22 1
< 0.1%
-60512.15 1
< 0.1%
-59207.35 1
< 0.1%
-54707.69 1
< 0.1%
-50950.68 1
< 0.1%
-50197.38 1
< 0.1%
-48064.46 1
< 0.1%
ValueCountFrequency (%)
5260.59 1
 
< 0.1%
5044.85 1
 
< 0.1%
2149.1 1
 
< 0.1%
1520.26 1
 
< 0.1%
817.74 1
 
< 0.1%
448.55 1
 
< 0.1%
262 1
 
< 0.1%
139 1
 
< 0.1%
9.49 1
 
< 0.1%
0 78
< 0.1%

misc_expenses
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct304
Distinct (%)56.0%
Missing3067497
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-1067.0834
Minimum-12737.33
Maximum3727.13
Zeros27
Zeros (%)< 0.1%
Negative509
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:44.166720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-12737.33
5-th percentile-5024.25
Q1-1236.665
median-485
Q3-160
95-th percentile0
Maximum3727.13
Range16464.46
Interquartile range (IQR)1076.665

Descriptive statistics

Standard deviation1753.9676
Coefficient of variation (CV)-1.6437025
Kurtosis12.46609
Mean-1067.0834
Median Absolute Deviation (MAD)393.98
Skewness-3.0978085
Sum-579426.26
Variance3076402.4
MonotonicityNot monotonic
2023-11-13T11:46:44.341985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-145 57
 
< 0.1%
-255 29
 
< 0.1%
0 27
 
< 0.1%
-290 22
 
< 0.1%
-110 17
 
< 0.1%
-85 11
 
< 0.1%
-200 9
 
< 0.1%
-400 7
 
< 0.1%
-151.95 7
 
< 0.1%
-220 6
 
< 0.1%
Other values (294) 351
 
< 0.1%
(Missing) 3067497
> 99.9%
ValueCountFrequency (%)
-12737.33 1
< 0.1%
-11901.17 1
< 0.1%
-10923.75 1
< 0.1%
-9170 1
< 0.1%
-8930.87 1
< 0.1%
-8705 1
< 0.1%
-8374.59 1
< 0.1%
-8075 1
< 0.1%
-8051.42 1
< 0.1%
-7447.96 1
< 0.1%
ValueCountFrequency (%)
3727.13 1
 
< 0.1%
2930 1
 
< 0.1%
2890.57 1
 
< 0.1%
2039.58 1
 
< 0.1%
1806.67 1
 
< 0.1%
1670.09 1
 
< 0.1%
1054.81 1
 
< 0.1%
0 27
< 0.1%
-30 3
 
< 0.1%
-85 11
< 0.1%

actual_loss
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct529
Distinct (%)97.1%
Missing3067495
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-65237.152
Minimum-557148.85
Maximum52706.85
Zeros16
Zeros (%)< 0.1%
Negative517
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:44.509650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-557148.85
5-th percentile-172997.44
Q1-89124.59
median-52205.88
Q3-26639.02
95-th percentile-5
Maximum52706.85
Range609855.7
Interquartile range (IQR)62485.57

Descriptive statistics

Standard deviation58641.801
Coefficient of variation (CV)-0.89890191
Kurtosis10.344934
Mean-65237.152
Median Absolute Deviation (MAD)29799.35
Skewness-2.178577
Sum-35554248
Variance3.4388608 × 109
MonotonicityNot monotonic
2023-11-13T11:46:44.658994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
< 0.1%
-110 2
 
< 0.1%
-230555.66 1
 
< 0.1%
-94861.55 1
 
< 0.1%
-5102.5 1
 
< 0.1%
-73678.72 1
 
< 0.1%
-4943.24 1
 
< 0.1%
-23781.58 1
 
< 0.1%
-99080.84 1
 
< 0.1%
-147974.29 1
 
< 0.1%
Other values (519) 519
 
< 0.1%
(Missing) 3067495
> 99.9%
ValueCountFrequency (%)
-557148.85 1
< 0.1%
-299821.11 1
< 0.1%
-298447.32 1
< 0.1%
-293023.55 1
< 0.1%
-273379.99 1
< 0.1%
-272685.82 1
< 0.1%
-248882.24 1
< 0.1%
-236209.83 1
< 0.1%
-235911.92 1
< 0.1%
-232226.45 1
< 0.1%
ValueCountFrequency (%)
52706.85 1
< 0.1%
52513.75 1
< 0.1%
2161 1
< 0.1%
2022.93 1
< 0.1%
2008.4 1
< 0.1%
1570.94 1
< 0.1%
1339.65 1
< 0.1%
1250.37 1
< 0.1%
632.59 1
< 0.1%
624.72 1
< 0.1%

mod_cost
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct460
Distinct (%)100.0%
Missing3067580
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7475.585
Minimum-8011.44
Maximum79408.85
Zeros0
Zeros (%)0.0%
Negative54
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:44.827623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8011.44
5-th percentile-430.3945
Q1338.1975
median1645.415
Q310016.298
95-th percentile32696.984
Maximum79408.85
Range87420.29
Interquartile range (IQR)9678.1

Descriptive statistics

Standard deviation12176.01
Coefficient of variation (CV)1.6287701
Kurtosis8.7247188
Mean7475.585
Median Absolute Deviation (MAD)1772.715
Skewness2.6365947
Sum3438769.1
Variance1.4825521 × 108
MonotonicityNot monotonic
2023-11-13T11:46:45.011122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21252.86 1
 
< 0.1%
38068.87 1
 
< 0.1%
11573.13 1
 
< 0.1%
36174.35 1
 
< 0.1%
3279.24 1
 
< 0.1%
21438.27 1
 
< 0.1%
-387.04 1
 
< 0.1%
-52.49 1
 
< 0.1%
6974.29 1
 
< 0.1%
39751.22 1
 
< 0.1%
Other values (450) 450
 
< 0.1%
(Missing) 3067580
> 99.9%
ValueCountFrequency (%)
-8011.44 1
< 0.1%
-4200.31 1
< 0.1%
-2484.92 1
< 0.1%
-2235.98 1
< 0.1%
-2068.46 1
< 0.1%
-1548.76 1
< 0.1%
-1342.89 1
< 0.1%
-1318.23 1
< 0.1%
-1168.81 1
< 0.1%
-1039.08 1
< 0.1%
ValueCountFrequency (%)
79408.85 1
< 0.1%
77071.58 1
< 0.1%
69270.11 1
< 0.1%
67728.15 1
< 0.1%
59049.17 1
< 0.1%
51152.84 1
< 0.1%
49006.37 1
< 0.1%
48822.5 1
< 0.1%
48594.77 1
< 0.1%
47722.3 1
< 0.1%

step_mod
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing3067566
Missing (%)> 99.9%
Memory size5.9 MiB
False
 
474
(Missing)
3067566 
ValueCountFrequency (%)
False 474
 
< 0.1%
(Missing) 3067566
> 99.9%
2023-11-13T11:46:45.185900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

deferred_payment_plan
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3063801
Missing (%)99.9%
Memory size23.4 MiB
P
4030 
Y
 
209

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowP
3rd rowP
4th rowP
5th rowP

Common Values

ValueCountFrequency (%)
P 4030
 
0.1%
Y 209
 
< 0.1%
(Missing) 3063801
99.9%

Length

2023-11-13T11:46:45.304882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T11:46:45.494486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
p 4030
95.1%
y 209
 
4.9%

Most occurring characters

ValueCountFrequency (%)
P 4030
95.1%
Y 209
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4239
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 4030
95.1%
Y 209
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4239
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 4030
95.1%
Y 209
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 4030
95.1%
Y 209
 
4.9%

eltv
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct195
Distinct (%)< 0.1%
Missing2581932
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean143.67751
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:45.767672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q124
median40
Q358
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)34

Descriptive statistics

Standard deviation302.17442
Coefficient of variation (CV)2.1031435
Kurtosis4.1155926
Mean143.67751
Median Absolute Deviation (MAD)17
Skewness2.4654187
Sum69842786
Variance91309.378
MonotonicityNot monotonic
2023-11-13T11:46:46.066236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 53759
 
1.8%
32 8300
 
0.3%
33 8188
 
0.3%
34 8135
 
0.3%
38 8100
 
0.3%
31 8057
 
0.3%
36 8033
 
0.3%
35 7987
 
0.3%
30 7956
 
0.3%
37 7904
 
0.3%
Other values (185) 359689
 
11.7%
(Missing) 2581932
84.2%
ValueCountFrequency (%)
1 1761
 
0.1%
2 2289
0.1%
3 2804
0.1%
4 3562
0.1%
5 3972
0.1%
6 4277
0.1%
7 4536
0.1%
8 4635
0.2%
9 4728
0.2%
10 4765
0.2%
ValueCountFrequency (%)
999 53759
1.8%
211 1
 
< 0.1%
207 1
 
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
203 1
 
< 0.1%
202 3
 
< 0.1%
201 1
 
< 0.1%
200 2
 
< 0.1%
199 1
 
< 0.1%

zero_bal_removal_upb
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45358
Distinct (%)97.4%
Missing3021495
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean188165.85
Minimum0.01
Maximum774925.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:46.334597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile26991.266
Q1101081.99
median166741.46
Q3258113.82
95-th percentile397756.74
Maximum774925.4
Range774925.39
Interquartile range (IQR)157031.83

Descriptive statistics

Standard deviation117536.71
Coefficient of variation (CV)0.62464424
Kurtosis0.99667763
Mean188165.85
Median Absolute Deviation (MAD)74119.63
Skewness0.88347681
Sum8.7581793 × 109
Variance1.3814878 × 1010
MonotonicityNot monotonic
2023-11-13T11:46:46.573284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415000 24
 
< 0.1%
414000 24
 
< 0.1%
416000 18
 
< 0.1%
409000 11
 
< 0.1%
100000 9
 
< 0.1%
115000 8
 
< 0.1%
399139.31 8
 
< 0.1%
305000 8
 
< 0.1%
412000 8
 
< 0.1%
397166.66 8
 
< 0.1%
Other values (45348) 46419
 
1.5%
(Missing) 3021495
98.5%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.14 1
< 0.1%
1 1
< 0.1%
3.14 1
< 0.1%
3.58 1
< 0.1%
5.18 1
< 0.1%
10.34 1
< 0.1%
11.99 1
< 0.1%
16.8 1
< 0.1%
19.87 1
< 0.1%
ValueCountFrequency (%)
774925.4 1
< 0.1%
751728.39 1
< 0.1%
747818.65 1
< 0.1%
746488.3 1
< 0.1%
728000 1
< 0.1%
727000 1
< 0.1%
726000 1
< 0.1%
725000 1
< 0.1%
724000 1
< 0.1%
722950.12 1
< 0.1%

delinq_int
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct575
Distinct (%)85.9%
Missing3067371
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean12658.866
Minimum-1142.16
Maximum170239.2
Zeros95
Zeros (%)< 0.1%
Negative5
Negative (%)< 0.1%
Memory size23.4 MiB
2023-11-13T11:46:46.846387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1142.16
5-th percentile0
Q12644.81
median7903.53
Q316605.27
95-th percentile40569.85
Maximum170239.2
Range171381.36
Interquartile range (IQR)13960.46

Descriptive statistics

Standard deviation16275.731
Coefficient of variation (CV)1.2857179
Kurtosis22.565506
Mean12658.866
Median Absolute Deviation (MAD)6741.33
Skewness3.622199
Sum8468781.1
Variance2.648994 × 108
MonotonicityNot monotonic
2023-11-13T11:46:47.084411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
< 0.1%
6221.7 1
 
< 0.1%
3083.33 1
 
< 0.1%
13372.24 1
 
< 0.1%
23856.68 1
 
< 0.1%
13386.54 1
 
< 0.1%
4628.68 1
 
< 0.1%
899.69 1
 
< 0.1%
10339.52 1
 
< 0.1%
6347.77 1
 
< 0.1%
Other values (565) 565
 
< 0.1%
(Missing) 3067371
> 99.9%
ValueCountFrequency (%)
-1142.16 1
 
< 0.1%
-858.52 1
 
< 0.1%
-742.22 1
 
< 0.1%
-223.42 1
 
< 0.1%
-103.04 1
 
< 0.1%
0 95
< 0.1%
102.11 1
 
< 0.1%
189.42 1
 
< 0.1%
222.77 1
 
< 0.1%
264.35 1
 
< 0.1%
ValueCountFrequency (%)
170239.2 1
< 0.1%
142270.57 1
< 0.1%
111369.88 1
< 0.1%
87588.12 1
< 0.1%
79657.68 1
< 0.1%
77541.6 1
< 0.1%
75923.28 1
< 0.1%
74910.77 1
< 0.1%
73377.54 1
< 0.1%
72797.87 1
< 0.1%

delinq_disaster
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing3064125
Missing (%)99.9%
Memory size5.9 MiB
True
 
3915
(Missing)
3064125 
ValueCountFrequency (%)
True 3915
 
0.1%
(Missing) 3064125
99.9%
2023-11-13T11:46:47.303093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

borrower_assistance
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing3062304
Missing (%)99.8%
Memory size23.4 MiB
F
3672 
T
1514 
R
550 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 3672
 
0.1%
T 1514
 
< 0.1%
R 550
 
< 0.1%
(Missing) 3062304
99.8%

Length

2023-11-13T11:46:47.440675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-13T11:46:47.595809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
f 3672
64.0%
t 1514
26.4%
r 550
 
9.6%

Most occurring characters

ValueCountFrequency (%)
F 3672
64.0%
T 1514
26.4%
R 550
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5736
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 3672
64.0%
T 1514
26.4%
R 550
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5736
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 3672
64.0%
T 1514
26.4%
R 550
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 3672
64.0%
T 1514
26.4%
R 550
 
9.6%

curr_month_mod_cost
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11334
Distinct (%)43.4%
Missing3041908
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean131.59227
Minimum-133.11
Maximum5128.74
Zeros5969
Zeros (%)0.2%
Negative2508
Negative (%)0.1%
Memory size23.4 MiB
2023-11-13T11:46:47.756903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-133.11
5-th percentile-9.92
Q10
median71.19
Q3191.4725
95-th percentile470.49
Maximum5128.74
Range5261.85
Interquartile range (IQR)191.4725

Descriptive statistics

Standard deviation200.81472
Coefficient of variation (CV)1.5260374
Kurtosis96.11055
Mean131.59227
Median Absolute Deviation (MAD)71.19
Skewness6.1201803
Sum3438769.1
Variance40326.553
MonotonicityNot monotonic
2023-11-13T11:46:47.935479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5969
 
0.2%
9.84 63
 
< 0.1%
11.46 61
 
< 0.1%
2.54 51
 
< 0.1%
7.9 50
 
< 0.1%
157.79 49
 
< 0.1%
206.61 48
 
< 0.1%
470.49 41
 
< 0.1%
10.08 40
 
< 0.1%
352 39
 
< 0.1%
Other values (11324) 19721
 
0.6%
(Missing) 3041908
99.1%
ValueCountFrequency (%)
-133.11 2
 
< 0.1%
-118.32 1
 
< 0.1%
-103.53 1
 
< 0.1%
-88.74 1
 
< 0.1%
-83.2 1
 
< 0.1%
-83.15 1
 
< 0.1%
-83.05 1
 
< 0.1%
-82.99 7
< 0.1%
-79.89 1
 
< 0.1%
-79.81 1
 
< 0.1%
ValueCountFrequency (%)
5128.74 2
< 0.1%
4558.88 1
< 0.1%
4519.2 2
< 0.1%
4142.6 1
< 0.1%
3989.02 1
< 0.1%
3766 1
< 0.1%
3419.16 1
< 0.1%
3389.4 1
< 0.1%
3017.52 1
< 0.1%
3012.8 1
< 0.1%

interest_bearing_upb
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2239895
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169408.94
Minimum0
Maximum790000
Zeros46545
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size23.4 MiB
2023-11-13T11:46:48.137649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36137.33
Q190924.22
median146121.67
Q3227811.16
95-th percentile383735.64
Maximum790000
Range790000
Interquartile range (IQR)136886.94

Descriptive statistics

Standard deviation107079.89
Coefficient of variation (CV)0.63207932
Kurtosis1.3503241
Mean169408.94
Median Absolute Deviation (MAD)63987.135
Skewness1.0484879
Sum5.197534 × 1011
Variance1.1466102 × 1010
MonotonicityNot monotonic
2023-11-13T11:46:48.328594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46545
 
1.5%
415000 3733
 
0.1%
416000 3713
 
0.1%
414000 3353
 
0.1%
100000 2684
 
0.1%
199000 2358
 
0.1%
99000 2067
 
0.1%
149000 2022
 
0.1%
417000 1991
 
0.1%
139000 1804
 
0.1%
Other values (2239885) 2997770
97.7%
ValueCountFrequency (%)
0 46545
1.5%
0.01 4
 
< 0.1%
0.14 1
 
< 0.1%
1 1
 
< 0.1%
3.14 1
 
< 0.1%
3.58 1
 
< 0.1%
5.18 1
 
< 0.1%
10.34 1
 
< 0.1%
11.99 1
 
< 0.1%
16.8 1
 
< 0.1%
ValueCountFrequency (%)
790000 1
 
< 0.1%
789000 1
 
< 0.1%
788000 2
< 0.1%
787000 2
< 0.1%
786000 3
< 0.1%
785000 1
 
< 0.1%
784000 2
< 0.1%
783000 1
 
< 0.1%
782959.35 1
 
< 0.1%
782000 1
 
< 0.1%

Interactions

2023-11-13T11:45:29.877638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:24.895579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:38.012581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:50.471638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:03.256464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:15.148440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:20.024575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:25.869047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:33.356343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:46.673673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:58.210018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:03.808096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:09.327794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:15.039182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:21.358191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:32.585217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:37.895178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:43.430375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:48.864103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:54.458727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:00.655412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:07.054945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:13.692913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:19.170976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:31.246927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:26.226580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:39.170895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:51.752802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:04.339107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:15.351099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:20.258066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:26.135680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:34.655519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:47.794703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:58.443625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:04.002194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:09.538208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:15.296911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:21.578066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:32.825676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:38.161321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:43.678275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:49.052161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:54.684996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:01.063627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:07.389119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:13.922102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:19.366890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:32.921041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:27.433498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:40.400080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:53.015074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:05.581075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:15.576007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:20.475185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:26.426729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:36.229410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:48.947072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:58.680752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:04.231210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:09.733456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:15.546121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:21.867236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:33.029928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:38.403050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:43.891944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:49.225834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:54.884767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:01.445305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:07.778810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:14.136848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:19.610879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:34.287234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:28.488620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:41.571043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:54.217045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:06.720298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:15.809421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:20.662953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:26.643834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:37.386622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:50.111268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:58.905898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:04.488887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:09.939445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:15.902169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:22.213933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:33.241045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:38.641008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:44.122058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:49.436925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:55.090146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:01.796345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:08.107085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:14.376193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:19.852935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:34.482354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:28.739144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:41.787717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:54.404750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:06.926950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:15.999544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:20.864089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:26.824919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:37.576755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:50.361329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:59.110792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:04.702986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:10.133569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:16.143713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:22.482867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:33.431081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:38.842641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:44.315195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:49.652055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:55.294610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:01.987024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:08.391721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:14.621339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:20.173123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:34.670446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:29.491750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:42.010704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:54.626526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:07.167025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:16.197914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:21.090400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:27.009067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:37.769764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:50.603551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:59.290921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:04.911357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:10.327715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:16.348878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:22.711150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:33.638228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:39.007968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:44.500329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:49.874748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:55.514056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:02.151929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:08.660790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:14.829835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:20.483276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:34.884437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:29.718473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:42.248307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:54.892207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:07.404070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:16.384881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:21.417134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:27.194144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:37.999625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:50.843557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:59.515105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:05.134794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:10.575563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:16.549210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:22.930818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:33.857286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:39.219744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:44.714867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:50.103095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:44:55.739178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:02.351596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:08.949699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:15.080262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:20.750222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:36.146025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:30.900310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:43.468891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:42:56.161476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:43:08.623245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-11-13T11:45:00.202442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:06.794483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:13.432411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:18.982193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-13T11:45:27.974715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-11-13T11:46:48.575288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
year_monthcurr_upbloan_agemonth_remaining_LMdefect_settlementzero_bal_codezero_bal_datecurr_intcurr_deferred_upbddlpimi_recnet_salesnon_mi_recexpenseslegal_costsmaint_coststax_insmisc_expensesactual_lossmod_costeltvzero_bal_removal_upbdelinq_intcurr_month_mod_costinterest_bearing_upbmod_flagdeferred_payment_planborrower_assistance
year_month1.000-0.3160.978-0.6250.9990.0511.0000.0040.0650.991-0.026-0.0280.032-0.366-0.320-0.231-0.253-0.4700.016-0.208-0.314-0.362-0.251-0.184-0.3160.1410.2020.516
curr_upb-0.3161.000-0.3150.412NaNNaNNaN-0.0290.005NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.0910.388NaNNaN0.4111.0000.0250.0000.105
loan_age0.978-0.3151.000-0.6410.8620.0060.972-0.009-0.0330.899-0.019-0.115-0.012-0.324-0.284-0.237-0.220-0.3600.0040.464-0.330-0.3620.2050.098-0.3150.2090.1520.470
month_remaining_LM-0.6250.412-0.6411.000-0.5600.057-0.5870.2980.006-0.6560.0250.1590.0540.2430.2040.1900.1560.239-0.0510.2110.5030.451-0.2940.0560.4120.0900.0190.487
defect_settlement0.999NaN0.862-0.5601.000-0.3191.000-0.079NaN0.859NaNNaNNaNNaNNaNNaNNaNNaNNaN0.500NaN0.132NaN0.462NaN1.0000.0000.000
zero_bal_code0.051NaN0.0060.057-0.3191.0000.0510.052NaN-0.4570.001-0.004-0.016-0.350-0.122-0.455-0.168-0.201-0.1210.101-0.4280.001-0.3210.047NaN1.0000.0000.000
zero_bal_date1.000NaN0.972-0.5871.0000.0511.000-0.054NaN0.991-0.026-0.0280.032-0.366-0.320-0.231-0.253-0.4700.016-0.101-0.134-0.362-0.251-0.078NaN1.0000.0000.000
curr_int0.004-0.029-0.0090.298-0.0790.052-0.0541.000-0.011-0.142-0.088-0.172-0.0420.038-0.0410.0150.0040.077-0.042-0.6300.2120.0260.213-0.720-0.0290.0220.0080.099
curr_deferred_upb0.0650.005-0.0330.006NaNNaNNaN-0.0111.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.1940.023NaNNaN0.162-0.0020.0000.0650.032
ddlpi0.991NaN0.899-0.6560.859-0.4570.991-0.142NaN1.000-0.008-0.0330.012-0.055-0.065-0.0020.029-0.3170.214-0.1480.045-0.300-0.428-0.128NaN1.0000.2100.000
mi_rec-0.026NaN-0.0190.025NaN0.001-0.026-0.088NaN-0.0081.0000.0700.187-0.084-0.034-0.060-0.125-0.0460.1680.1750.2780.1070.0590.198NaN1.0001.0000.000
net_sales-0.028NaN-0.1150.159NaN-0.004-0.028-0.172NaN-0.0330.0701.0000.270-0.207-0.0690.030-0.3320.007-0.1700.3660.2350.8340.5780.239NaN1.0001.0000.000
non_mi_rec0.032NaN-0.0120.054NaN-0.0160.032-0.042NaN0.0120.1870.2701.000-0.304-0.206-0.191-0.406-0.169-0.1370.413-0.0030.2080.2370.220NaN1.0001.0000.000
expenses-0.366NaN-0.3240.243NaN-0.350-0.3660.038NaN-0.055-0.084-0.207-0.3041.0000.7310.7940.7990.5070.463-0.523-0.198-0.124-0.659-0.400NaN1.0001.0000.000
legal_costs-0.320NaN-0.2840.204NaN-0.122-0.320-0.041NaN-0.065-0.034-0.069-0.2060.7311.0000.5340.5700.3200.392-0.380-0.025-0.017-0.500-0.325NaN1.0001.0000.000
maint_costs-0.231NaN-0.2370.190NaN-0.455-0.2310.015NaN-0.002-0.0600.030-0.1910.7940.5341.0000.4390.4850.326-0.410-0.1810.101-0.410-0.335NaN1.0001.0000.000
tax_ins-0.253NaN-0.2200.156NaN-0.168-0.2530.004NaN0.029-0.125-0.332-0.4060.7990.5700.4391.0000.2400.455-0.591-0.177-0.288-0.691-0.355NaN1.0001.0000.000
misc_expenses-0.470NaN-0.3600.239NaN-0.201-0.4700.077NaN-0.317-0.0460.007-0.1690.5070.3200.4850.2401.0000.1660.156-0.0810.122-0.2550.208NaN1.0001.0000.000
actual_loss0.016NaN0.004-0.051NaN-0.1210.016-0.042NaN0.2140.168-0.170-0.1370.4630.3920.3260.4550.1661.000-0.448-0.382-0.391-0.592-0.437NaN1.0001.0000.000
mod_cost-0.208-0.0910.4640.2110.5000.101-0.101-0.630-0.194-0.1480.1750.3660.413-0.523-0.380-0.410-0.5910.156-0.4481.0000.2350.4640.0910.922-0.0921.0000.0001.000
eltv-0.3140.388-0.3300.503NaN-0.428-0.1340.2120.0230.0450.2780.235-0.003-0.198-0.025-0.181-0.177-0.081-0.3820.2351.000-0.0440.2200.3240.3880.0000.0000.020
zero_bal_removal_upb-0.362NaN-0.3620.4510.1320.001-0.3620.026NaN-0.3000.1070.8340.208-0.124-0.0170.101-0.2880.122-0.3910.464-0.0441.0000.3560.401NaN1.0000.0000.000
delinq_int-0.251NaN0.205-0.294NaN-0.321-0.2510.213NaN-0.4280.0590.5780.237-0.659-0.500-0.410-0.691-0.255-0.5920.0910.2200.3561.0000.206NaN1.0001.0000.000
curr_month_mod_cost-0.1840.4110.0980.0560.4620.047-0.078-0.7200.162-0.1280.1980.2390.220-0.400-0.325-0.335-0.3550.208-0.4370.9220.3240.4010.2061.0000.3780.0000.0000.058
interest_bearing_upb-0.3161.000-0.3150.412NaNNaNNaN-0.029-0.002NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.0920.388NaNNaN0.3781.0000.0190.0120.110
mod_flag0.1410.0250.2090.0901.0001.0001.0000.0220.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0000.0191.0000.0000.154
deferred_payment_plan0.2020.0000.1520.0190.0000.0000.0000.0080.0650.2101.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0000.0000.0120.0001.0000.146
borrower_assistance0.5160.1050.4700.4870.0000.0000.0000.0990.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0200.0000.0000.0580.1100.1540.1461.000

Missing values

2023-11-13T11:45:44.420459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-13T11:45:55.546256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-13T11:46:24.115563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loan_idyear_monthcurr_upbcurr_loan_delinqloan_agemonth_remaining_LMdefect_settlementmod_flagzero_bal_codezero_bal_datecurr_intcurr_deferred_upbddlpimi_recnet_salesnon_mi_recexpenseslegal_costsmaint_coststax_insmisc_expensesactual_lossmod_coststep_moddeferred_payment_planeltvzero_bal_removal_upbdelinq_intdelinq_disasterborrower_assistancecurr_month_mod_costinterest_bearing_upb
0F09Q10000013200902157000.0000180NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN157000.00
1F09Q10000013200903156000.0001179NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN156000.00
2F09Q10000013200904155000.0002178NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN155000.00
3F09Q10000013200905155000.0003177NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN155000.00
4F09Q10000013200906154000.0004176NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN154000.00
5F09Q10000013200907153000.0005175NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN153000.00
6F09Q10000013200908152000.0006174NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN152000.00
7F09Q10000013200909151743.4207173NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN151743.42
8F09Q10000013200910151042.8308172NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN151042.83
9F09Q10000013200911150339.6109171NaNNaNNaNNaN4.50.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN150339.61
loan_idyear_monthcurr_upbcurr_loan_delinqloan_agemonth_remaining_LMdefect_settlementmod_flagzero_bal_codezero_bal_datecurr_intcurr_deferred_upbddlpimi_recnet_salesnon_mi_recexpenseslegal_costsmaint_coststax_insmisc_expensesactual_lossmod_coststep_moddeferred_payment_planeltvzero_bal_removal_upbdelinq_intdelinq_disasterborrower_assistancecurr_month_mod_costinterest_bearing_upb
3068030F09Q4045194920220623813.900149211NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN25.0NaNNaNNaNNaNNaN23813.90
3068031F09Q4045194920220723813.900150210NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaN23813.90
3068032F09Q4045194920220823399.970151209NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20.0NaNNaNNaNNaNNaN23399.97
3068033F09Q4045194920220922567.060152208NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22.0NaNNaNNaNNaNNaN22567.06
3068034F09Q4045194920221022567.060153207NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaN22567.06
3068035F09Q4045194920221122148.070154206NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaN22148.07
3068036F09Q4045194920221221315.020155205NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaN21315.02
3068037F09Q4045194920230120874.780156204NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaN20874.78
3068038F09Q4045194920230220874.780157203NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23.0NaNNaNNaNNaNNaN20874.78
3068039F09Q4045194920230319968.890158202NaNNaNNaNNaN4.8750.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN999.0NaNNaNNaNNaNNaN19968.89